Phonemes and Syllables in Speech Perception: size of the attentional focus in French.

A study by Pitt and Samuel (1990) found that English speakers could narrowly focus attention onto a precise phonemic position inside spoken words [1]. This led the authors to argue that the phoneme, rather than the syllable, is the primary unit of speech perception. Other evidence, obtained with a syllable detection paradigm, has been put forward to propose that the syllable is the unit of perception; yet, these experiments were ran with French speakers [2]. In the present study, we adapted Pitt & Samuel's phoneme detection experiment to French and found that French subjects behave exactly like English subjects: they too can focus attention on a precise phoneme. To explain both this result and the established sensitivity to the syllabic structure, we propose that the perceptual system automatically parses the speech signal into a syllabically-structured phonological representation

Information theoretic syllable structure and its relation to the c-center effect

Established phonological theories postulate uniform syllable constituent structures. From a traditional hierarchical point of view, syllables are right branching implying a close connection between the nucleus and the coda. Articulatory Phonology in contrast suggests a stronger cohesion between onsets and nuclei than between nuclei and codas. This claim is empirically supported by the c-center effect which initially has been observed for onsets only. Nevertheless, recent studies revealed that this effect does not occur in all complex onsets and can also be observed in codas. To account for this structure non-uniformity, we propose an information theoretic approach to measure connection strengths between syllable constituents in terms of their pointwise mutual information. It turned out that the derived constituent structures correspond well to the empirical c-center findings on American English and German data. The results are discussed from a Usage-based Phonology perspective considering c-centers to be a frequency effect

Production and perception of speaker-specific phonetic detail at word boundaries

Experiments show that learning about familiar voices affects speech processing in many tasks. However, most studies focus on isolated phonemes or words and do not explore which phonetic properties are learned about or retained in memory. This work investigated inter-speaker phonetic variation involving word boundaries, and its perceptual consequences. A production experiment found significant variation in the extent to which speakers used a number of acoustic properties to distinguish junctural minimal pairs e.g. 'So he diced them'—'So he'd iced them'. A perception experiment then tested intelligibility in noise of the junctural minimal pairs before and after familiarisation with a particular voice. Subjects who heard the same voice during testing as during the familiarisation period showed significantly more improvement in identification of words and syllable constituents around word boundaries than those who heard different voices. These data support the view that perceptual learning about the particular pronunciations associated with individual speakers helps listeners to identify syllabic structure and the location of word boundaries

A role for the developing lexicon in phonetic category acquisition

Infants segment words from fluent speech during the same period when they are learning phonetic categories, yet accounts of phonetic category acquisition typically ignore information about the words in which sounds appear. We use a Bayesian model to illustrate how feedback from segmented words might constrain phonetic category learning by providing information about which sounds occur together in words. Simulations demonstrate that word-level information can successfully disambiguate overlapping English vowel categories. Learning patterns in the model are shown to parallel human behavior from artificial language learning tasks. These findings point to a central role for the developing lexicon in phonetic category acquisition and provide a framework for incorporating top-down constraints into models of category learning

Improving Statistical Language Model Performance with Automatically Generated Word Hierarchies

An automatic word classification system has been designed which processes word unigram and bigram frequency statistics extracted from a corpus of natural language utterances. The system implements a binary top-down form of word clustering which employs an average class mutual information metric. Resulting classifications are hierarchical, allowing variable class granularity. Words are represented as structural tags --- unique $n$-bit numbers the most significant bit-patterns of which incorporate class information. Access to a structural tag immediately provides access to all classification levels for the corresponding word. The classification system has successfully revealed some of the structure of English, from the phonemic to the semantic level. The system has been compared --- directly and indirectly --- with other recent word classification systems. Class based interpolated language models have been constructed to exploit the extra information supplied by the classifications and some experiments have shown that the new models improve model performance.Comment: 17 Page Paper. Self-extracting PostScript Fil

A High Quality Text-To-Speech System Composed of Multiple Neural Networks

While neural networks have been employed to handle several different text-to-speech tasks, ours is the first system to use neural networks throughout, for both linguistic and acoustic processing. We divide the text-to-speech task into three subtasks, a linguistic module mapping from text to a linguistic representation, an acoustic module mapping from the linguistic representation to speech, and a video module mapping from the linguistic representation to animated images. The linguistic module employs a letter-to-sound neural network and a postlexical neural network. The acoustic module employs a duration neural network and a phonetic neural network. The visual neural network is employed in parallel to the acoustic module to drive a talking head. The use of neural networks that can be retrained on the characteristics of different voices and languages affords our system a degree of adaptability and naturalness heretofore unavailable.Comment: Source link (9812006.tar.gz) contains: 1 PostScript file (4 pages) and 3 WAV audio files. If your system does not support Windows WAV files, try a tool like "sox" to translate the audio into a format of your choic

Relationships Between Vocabulary Size, Working Memory, and Phonological Awareness in Spanish-Speaking English Language Learners

Purpose: The goals of this study were to evaluate the impact of short-term phonological awareness (PA) instruction presented in children\u27s first language (L1; Spanish) on gains in their L1 and second language (L2; English) and to determine whether relationships exist between vocabulary size, verbal working memory, and PA in Spanish-speaking English language learners (ELLs). Method: Participants included 25 kindergartners who received PA instruction and 10 controls. A 2-way within-subjects repeated measures multivariate analysis of variance (MANOVA) was conducted to evaluate gains. Relationships between PA gains, Spanish and English vocabulary, and memory, as measured using nonword repetition and experimental working memory tasks, were analyzed using correlation and regression analyses. Results: Results indicated significant and equivalent gains in both languages of children in the experimental group and no gains in the control group. Spanish vocabulary size was significantly related to PA gains in both languages and was more strongly related to English gains than was English vocabulary size. The memory tasks predicted gains in each language in distinct ways. Conclusion: Results support the conclusion that PA instruction and strong vocabulary skills in an individual\u27s L1 benefit PA development in both the L1 and L2. Results also indicate that dynamic relationships exist between vocabulary size, storage and processing components of working memory, and PA development in both languages of ELLs